Overview

Dataset statistics

Number of variables27
Number of observations46228
Missing cells48847
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 MiB
Average record size in memory216.0 B

Variable types

Numeric13
Categorical14

Alerts

country has a high cardinality: 150 distinct values High cardinality
agent has 5522 (11.9%) missing values Missing
company has 43323 (93.7%) missing values Missing
previous_cancellations is highly skewed (γ1 = 24.57744197) Skewed
df_index has unique values Unique
lead_time has 2836 (6.1%) zeros Zeros
previous_cancellations has 45857 (99.2%) zeros Zeros
previous_bookings_not_canceled has 44761 (96.8%) zeros Zeros
booking_changes has 37639 (81.4%) zeros Zeros
days_in_waiting_list has 45127 (97.6%) zeros Zeros
total_of_special_requests has 21617 (46.8%) zeros Zeros

Reproduction

Analysis started2023-01-20 05:17:03.111416
Analysis finished2023-01-20 05:18:05.909039
Duration1 minute and 2.8 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct46228
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92264.34827
Minimum40060
Maximum119389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:06.219508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40060
5-th percentile43378.45
Q184651.75
median96254.5
Q3107822.25
95-th percentile117076.65
Maximum119389
Range79329
Interquartile range (IQR)23170.5

Descriptive statistics

Standard deviation21051.41383
Coefficient of variation (CV)0.22816412
Kurtosis0.5861398278
Mean92264.34827
Median Absolute Deviation (MAD)11585.5
Skewness-1.136907785
Sum4265196292
Variance443162024.2
MonotonicityStrictly increasing
2023-01-20T00:18:06.873182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400601
 
< 0.1%
1039591
 
< 0.1%
1039611
 
< 0.1%
1039621
 
< 0.1%
1039631
 
< 0.1%
1039641
 
< 0.1%
1039651
 
< 0.1%
1039661
 
< 0.1%
1039671
 
< 0.1%
1039681
 
< 0.1%
Other values (46218)46218
> 99.9%
ValueCountFrequency (%)
400601
< 0.1%
400661
< 0.1%
400701
< 0.1%
400711
< 0.1%
400721
< 0.1%
400731
< 0.1%
400751
< 0.1%
400771
< 0.1%
400781
< 0.1%
400821
< 0.1%
ValueCountFrequency (%)
1193891
< 0.1%
1193881
< 0.1%
1193871
< 0.1%
1193861
< 0.1%
1193851
< 0.1%
1193841
< 0.1%
1193831
< 0.1%
1193821
< 0.1%
1193811
< 0.1%
1193801
< 0.1%

lead_time
Real number (ℝ≥0)

ZEROS

Distinct384
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.70273427
Minimum0
Maximum518
Zeros2836
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:07.329244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median50
Q3121
95-th percentile265
Maximum518
Range518
Interquartile range (IQR)109

Descriptive statistics

Standard deviation89.8630283
Coefficient of variation (CV)1.113506613
Kurtosis2.901544293
Mean80.70273427
Median Absolute Deviation (MAD)45
Skewness1.640353121
Sum3730726
Variance8075.363856
MonotonicityNot monotonic
2023-01-20T00:18:07.613906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02836
 
6.1%
11632
 
3.5%
21003
 
2.2%
4920
 
2.0%
3903
 
2.0%
5811
 
1.8%
6743
 
1.6%
7641
 
1.4%
8586
 
1.3%
12530
 
1.1%
Other values (374)35623
77.1%
ValueCountFrequency (%)
02836
6.1%
11632
3.5%
21003
 
2.2%
3903
 
2.0%
4920
 
2.0%
5811
 
1.8%
6743
 
1.6%
7641
 
1.4%
8586
 
1.3%
9469
 
1.0%
ValueCountFrequency (%)
51822
< 0.1%
50421
< 0.1%
47920
< 0.1%
4782
 
< 0.1%
47615
< 0.1%
46813
< 0.1%
46516
< 0.1%
4644
 
< 0.1%
4631
 
< 0.1%
46221
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
2016
22733 
2017
15817 
2015
7678 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters184912
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
201622733
49.2%
201715817
34.2%
20157678
 
16.6%

Length

2023-01-20T00:18:07.831446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:08.108190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
201622733
49.2%
201715817
34.2%
20157678
 
16.6%

Most occurring characters

ValueCountFrequency (%)
246228
25.0%
046228
25.0%
146228
25.0%
622733
12.3%
715817
 
8.6%
57678
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number184912
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
246228
25.0%
046228
25.0%
146228
25.0%
622733
12.3%
715817
 
8.6%
57678
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common184912
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246228
25.0%
046228
25.0%
146228
25.0%
622733
12.3%
715817
 
8.6%
57678
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII184912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246228
25.0%
046228
25.0%
146228
25.0%
622733
12.3%
715817
 
8.6%
57678
 
4.2%

arrival_date_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.546054339
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:08.281547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.073563744
Coefficient of variation (CV)0.4695292133
Kurtosis-0.9989738283
Mean6.546054339
Median Absolute Deviation (MAD)2
Skewness-0.03911360686
Sum302611
Variance9.446794088
MonotonicityNot monotonic
2023-01-20T00:18:08.467691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
85381
11.6%
74782
10.3%
54579
9.9%
64366
9.4%
104337
9.4%
94290
9.3%
34072
8.8%
44015
8.7%
23064
6.6%
112696
5.8%
Other values (2)4646
10.1%
ValueCountFrequency (%)
12254
4.9%
23064
6.6%
34072
8.8%
44015
8.7%
54579
9.9%
64366
9.4%
74782
10.3%
85381
11.6%
94290
9.3%
104337
9.4%
ValueCountFrequency (%)
122392
5.2%
112696
5.8%
104337
9.4%
94290
9.3%
85381
11.6%
74782
10.3%
64366
9.4%
54579
9.9%
44015
8.7%
34072
8.8%

arrival_date_week_number
Real number (ℝ≥0)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.15953535
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:08.724595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median27
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.56208133
Coefficient of variation (CV)0.4993487982
Kurtosis-0.9898022355
Mean27.15953535
Median Absolute Deviation (MAD)11
Skewness-0.02033523998
Sum1255531
Variance183.9300501
MonotonicityNot monotonic
2023-01-20T00:18:08.913305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331305
 
2.8%
341263
 
2.7%
211159
 
2.5%
321157
 
2.5%
271121
 
2.4%
301119
 
2.4%
381101
 
2.4%
391098
 
2.4%
281078
 
2.3%
411072
 
2.3%
Other values (43)34755
75.2%
ValueCountFrequency (%)
1440
1.0%
2448
1.0%
3488
1.1%
4527
1.1%
5527
1.1%
6589
1.3%
7793
1.7%
8865
1.9%
9808
1.7%
10899
1.9%
ValueCountFrequency (%)
53692
1.5%
52412
0.9%
51330
 
0.7%
50540
1.2%
49602
1.3%
48713
1.5%
47703
1.5%
46595
1.3%
45646
1.4%
44979
2.1%

arrival_date_day_of_month
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.81861642
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:09.124394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.729888951
Coefficient of variation (CV)0.5518743686
Kurtosis-1.191748227
Mean15.81861642
Median Absolute Deviation (MAD)8
Skewness-0.01571243558
Sum731263
Variance76.2109611
MonotonicityNot monotonic
2023-01-20T00:18:09.350072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
251736
 
3.8%
61657
 
3.6%
201653
 
3.6%
21648
 
3.6%
51634
 
3.5%
191616
 
3.5%
181603
 
3.5%
171598
 
3.5%
261587
 
3.4%
241578
 
3.4%
Other values (21)29918
64.7%
ValueCountFrequency (%)
11280
2.8%
21648
3.6%
31420
3.1%
41470
3.2%
51634
3.5%
61657
3.6%
71373
3.0%
81388
3.0%
91543
3.3%
101505
3.3%
ValueCountFrequency (%)
31817
1.8%
301265
2.7%
291427
3.1%
281532
3.3%
271495
3.2%
261587
3.4%
251736
3.8%
241578
3.4%
231537
3.3%
221346
2.9%

adults
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
2
32499 
1
10425 
3
 
2997
0
 
283
4
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46228
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
232499
70.3%
110425
 
22.6%
32997
 
6.5%
0283
 
0.6%
424
 
0.1%

Length

2023-01-20T00:18:09.560767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:09.715767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
232499
70.3%
110425
 
22.6%
32997
 
6.5%
0283
 
0.6%
424
 
0.1%

Most occurring characters

ValueCountFrequency (%)
232499
70.3%
110425
 
22.6%
32997
 
6.5%
0283
 
0.6%
424
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46228
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
232499
70.3%
110425
 
22.6%
32997
 
6.5%
0283
 
0.6%
424
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common46228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
232499
70.3%
110425
 
22.6%
32997
 
6.5%
0283
 
0.6%
424
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII46228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
232499
70.3%
110425
 
22.6%
32997
 
6.5%
0283
 
0.6%
424
 
0.1%

children
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
0.0
42934 
1.0
 
2014
2.0
 
1236
3.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters138684
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.042934
92.9%
1.02014
 
4.4%
2.01236
 
2.7%
3.044
 
0.1%

Length

2023-01-20T00:18:09.846922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:09.969996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.042934
92.9%
1.02014
 
4.4%
2.01236
 
2.7%
3.044
 
0.1%

Most occurring characters

ValueCountFrequency (%)
089162
64.3%
.46228
33.3%
12014
 
1.5%
21236
 
0.9%
344
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number92456
66.7%
Other Punctuation46228
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
089162
96.4%
12014
 
2.2%
21236
 
1.3%
344
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.46228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common138684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
089162
64.3%
.46228
33.3%
12014
 
1.5%
21236
 
0.9%
344
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII138684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
089162
64.3%
.46228
33.3%
12014
 
1.5%
21236
 
0.9%
344
 
< 0.1%

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
0
45923 
1
 
297
2
 
6
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.000021632
Min length1

Characters and Unicode

Total characters46229
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
045923
99.3%
1297
 
0.6%
26
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Length

2023-01-20T00:18:10.104301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:10.265129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
045923
99.3%
1297
 
0.6%
26
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
045924
99.3%
1298
 
0.6%
26
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46229
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
045924
99.3%
1298
 
0.6%
26
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common46229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
045924
99.3%
1298
 
0.6%
26
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII46229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
045924
99.3%
1298
 
0.6%
26
 
< 0.1%
91
 
< 0.1%

meal
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
BB
35638 
SC
6601 
HB
3980 
FB
 
9

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters92456
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHB
2nd rowHB
3rd rowHB
4th rowHB
5th rowHB

Common Values

ValueCountFrequency (%)
BB35638
77.1%
SC6601
 
14.3%
HB3980
 
8.6%
FB9
 
< 0.1%

Length

2023-01-20T00:18:10.412596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:10.556869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
bb35638
77.1%
sc6601
 
14.3%
hb3980
 
8.6%
fb9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B75265
81.4%
S6601
 
7.1%
C6601
 
7.1%
H3980
 
4.3%
F9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter92456
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B75265
81.4%
S6601
 
7.1%
C6601
 
7.1%
H3980
 
4.3%
F9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin92456
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B75265
81.4%
S6601
 
7.1%
C6601
 
7.1%
H3980
 
4.3%
F9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII92456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B75265
81.4%
S6601
 
7.1%
C6601
 
7.1%
H3980
 
4.3%
F9
 
< 0.1%

country
Categorical

HIGH CARDINALITY

Distinct150
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size361.3 KiB
PRT
10879 
FRA
7081 
DEU
5012 
GBR
3753 
ESP
3285 
Other values (145)
16216 

Length

Max length3
Median length3
Mean length2.9911089
Min length2

Characters and Unicode

Total characters138267
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowPRT
4th rowPRT
5th rowPRT

Common Values

ValueCountFrequency (%)
PRT10879
23.5%
FRA7081
15.3%
DEU5012
10.8%
GBR3753
 
8.1%
ESP3285
 
7.1%
ITA2054
 
4.4%
BEL1479
 
3.2%
NLD1259
 
2.7%
USA1189
 
2.6%
BRA1065
 
2.3%
Other values (140)9170
19.8%

Length

2023-01-20T00:18:10.702082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt10879
23.5%
fra7081
15.3%
deu5012
10.8%
gbr3753
 
8.1%
esp3285
 
7.1%
ita2054
 
4.4%
bel1479
 
3.2%
nld1259
 
2.7%
usa1189
 
2.6%
bra1065
 
2.3%
Other values (140)9170
19.8%

Most occurring characters

ValueCountFrequency (%)
R25823
18.7%
P14846
10.7%
T14105
10.2%
A13122
9.5%
E11637
8.4%
U8296
 
6.0%
F7366
 
5.3%
D6720
 
4.9%
B6543
 
4.7%
S6341
 
4.6%
Other values (16)23468
17.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter138267
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R25823
18.7%
P14846
10.7%
T14105
10.2%
A13122
9.5%
E11637
8.4%
U8296
 
6.0%
F7366
 
5.3%
D6720
 
4.9%
B6543
 
4.7%
S6341
 
4.6%
Other values (16)23468
17.0%

Most occurring scripts

ValueCountFrequency (%)
Latin138267
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R25823
18.7%
P14846
10.7%
T14105
10.2%
A13122
9.5%
E11637
8.4%
U8296
 
6.0%
F7366
 
5.3%
D6720
 
4.9%
B6543
 
4.7%
S6341
 
4.6%
Other values (16)23468
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII138267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R25823
18.7%
P14846
10.7%
T14105
10.2%
A13122
9.5%
E11637
8.4%
U8296
 
6.0%
F7366
 
5.3%
D6720
 
4.9%
B6543
 
4.7%
S6341
 
4.6%
Other values (16)23468
17.0%

market_segment
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
Online TA
24257 
Offline TA/TO
9574 
Direct
5037 
Groups
4352 
Corporate
 
2345
Other values (2)
 
663

Length

Max length13
Median length9
Mean length9.256467942
Min length6

Characters and Unicode

Total characters427908
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline TA/TO
2nd rowGroups
3rd rowGroups
4th rowGroups
5th rowGroups

Common Values

ValueCountFrequency (%)
Online TA24257
52.5%
Offline TA/TO9574
 
20.7%
Direct5037
 
10.9%
Groups4352
 
9.4%
Corporate2345
 
5.1%
Complementary478
 
1.0%
Aviation185
 
0.4%

Length

2023-01-20T00:18:10.926793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:11.199854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
online24257
30.3%
ta24257
30.3%
offline9574
 
12.0%
ta/to9574
 
12.0%
direct5037
 
6.3%
groups4352
 
5.4%
corporate2345
 
2.9%
complementary478
 
0.6%
aviation185
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n58751
13.7%
O43405
10.1%
T43405
10.1%
e42169
9.9%
i39238
9.2%
l34309
8.0%
A34016
7.9%
33831
7.9%
f19148
 
4.5%
r14557
 
3.4%
Other values (14)65079
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter251465
58.8%
Uppercase Letter133038
31.1%
Space Separator33831
 
7.9%
Other Punctuation9574
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n58751
23.4%
e42169
16.8%
i39238
15.6%
l34309
13.6%
f19148
 
7.6%
r14557
 
5.8%
o9705
 
3.9%
t8045
 
3.2%
p7175
 
2.9%
c5037
 
2.0%
Other values (6)13331
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O43405
32.6%
T43405
32.6%
A34016
25.6%
D5037
 
3.8%
G4352
 
3.3%
C2823
 
2.1%
Space Separator
ValueCountFrequency (%)
33831
100.0%
Other Punctuation
ValueCountFrequency (%)
/9574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin384503
89.9%
Common43405
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n58751
15.3%
O43405
11.3%
T43405
11.3%
e42169
11.0%
i39238
10.2%
l34309
8.9%
A34016
8.8%
f19148
 
5.0%
r14557
 
3.8%
o9705
 
2.5%
Other values (12)45800
11.9%
Common
ValueCountFrequency (%)
33831
77.9%
/9574
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII427908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n58751
13.7%
O43405
10.1%
T43405
10.1%
e42169
9.9%
i39238
9.2%
l34309
8.0%
A34016
7.9%
33831
7.9%
f19148
 
4.5%
r14557
 
3.4%
Other values (14)65079
15.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
TA/TO
37902 
Direct
5548 
Corporate
 
2622
GDS
 
156

Length

Max length9
Median length5
Mean length5.340140175
Min length3

Characters and Unicode

Total characters246864
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowTA/TO
3rd rowTA/TO
4th rowTA/TO
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO37902
82.0%
Direct5548
 
12.0%
Corporate2622
 
5.7%
GDS156
 
0.3%

Length

2023-01-20T00:18:11.395582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:11.540718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ta/to37902
82.0%
direct5548
 
12.0%
corporate2622
 
5.7%
gds156
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T75804
30.7%
A37902
15.4%
/37902
15.4%
O37902
15.4%
r10792
 
4.4%
e8170
 
3.3%
t8170
 
3.3%
D5704
 
2.3%
i5548
 
2.2%
c5548
 
2.2%
Other values (6)13422
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter160246
64.9%
Lowercase Letter48716
 
19.7%
Other Punctuation37902
 
15.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r10792
22.2%
e8170
16.8%
t8170
16.8%
i5548
11.4%
c5548
11.4%
o5244
10.8%
p2622
 
5.4%
a2622
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
T75804
47.3%
A37902
23.7%
O37902
23.7%
D5704
 
3.6%
C2622
 
1.6%
G156
 
0.1%
S156
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/37902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin208962
84.6%
Common37902
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T75804
36.3%
A37902
18.1%
O37902
18.1%
r10792
 
5.2%
e8170
 
3.9%
t8170
 
3.9%
D5704
 
2.7%
i5548
 
2.7%
c5548
 
2.7%
o5244
 
2.5%
Other values (5)8178
 
3.9%
Common
ValueCountFrequency (%)
/37902
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII246864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T75804
30.7%
A37902
15.4%
/37902
15.4%
O37902
15.4%
r10792
 
4.4%
e8170
 
3.3%
t8170
 
3.3%
D5704
 
2.3%
i5548
 
2.2%
c5548
 
2.2%
Other values (6)13422
 
5.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
0
44637 
1
 
1591

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46228
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
044637
96.6%
11591
 
3.4%

Length

2023-01-20T00:18:11.715426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:11.859304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
044637
96.6%
11591
 
3.4%

Most occurring characters

ValueCountFrequency (%)
044637
96.6%
11591
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46228
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
044637
96.6%
11591
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common46228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
044637
96.6%
11591
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII46228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
044637
96.6%
11591
 
3.4%

previous_cancellations
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02115600934
Minimum0
Maximum13
Zeros45857
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:11.953234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.336915105
Coefficient of variation (CV)15.92526736
Kurtosis718.655586
Mean0.02115600934
Median Absolute Deviation (MAD)0
Skewness24.57744197
Sum978
Variance0.113511788
MonotonicityNot monotonic
2023-01-20T00:18:12.073203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
045857
99.2%
1191
 
0.4%
259
 
0.1%
344
 
0.1%
1125
 
0.1%
421
 
< 0.1%
515
 
< 0.1%
615
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
045857
99.2%
1191
 
0.4%
259
 
0.1%
344
 
0.1%
421
 
< 0.1%
515
 
< 0.1%
615
 
< 0.1%
1125
 
0.1%
131
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
1125
 
0.1%
615
 
< 0.1%
515
 
< 0.1%
421
 
< 0.1%
344
 
0.1%
259
 
0.1%
1191
 
0.4%
045857
99.2%

previous_bookings_not_canceled
Real number (ℝ≥0)

ZEROS

Distinct73
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2085099939
Minimum0
Maximum72
Zeros44761
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:12.221179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.123927656
Coefficient of variation (CV)10.18621513
Kurtosis448.4554928
Mean0.2085099939
Median Absolute Deviation (MAD)0
Skewness18.62445817
Sum9639
Variance4.511068686
MonotonicityNot monotonic
2023-01-20T00:18:12.383118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044761
96.8%
1524
 
1.1%
2180
 
0.4%
3121
 
0.3%
493
 
0.2%
582
 
0.2%
656
 
0.1%
747
 
0.1%
836
 
0.1%
936
 
0.1%
Other values (63)292
 
0.6%
ValueCountFrequency (%)
044761
96.8%
1524
 
1.1%
2180
 
0.4%
3121
 
0.3%
493
 
0.2%
582
 
0.2%
656
 
0.1%
747
 
0.1%
836
 
0.1%
936
 
0.1%
ValueCountFrequency (%)
721
< 0.1%
711
< 0.1%
701
< 0.1%
691
< 0.1%
681
< 0.1%
671
< 0.1%
661
< 0.1%
651
< 0.1%
641
< 0.1%
631
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
A
35347 
D
7621 
F
 
1091
E
 
1048
B
 
747
Other values (2)
 
374

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46228
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A35347
76.5%
D7621
 
16.5%
F1091
 
2.4%
E1048
 
2.3%
B747
 
1.6%
G365
 
0.8%
C9
 
< 0.1%

Length

2023-01-20T00:18:12.532351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:12.743372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a35347
76.5%
d7621
 
16.5%
f1091
 
2.4%
e1048
 
2.3%
b747
 
1.6%
g365
 
0.8%
c9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A35347
76.5%
D7621
 
16.5%
F1091
 
2.4%
E1048
 
2.3%
B747
 
1.6%
G365
 
0.8%
C9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter46228
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A35347
76.5%
D7621
 
16.5%
F1091
 
2.4%
E1048
 
2.3%
B747
 
1.6%
G365
 
0.8%
C9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin46228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A35347
76.5%
D7621
 
16.5%
F1091
 
2.4%
E1048
 
2.3%
B747
 
1.6%
G365
 
0.8%
C9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII46228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A35347
76.5%
D7621
 
16.5%
F1091
 
2.4%
E1048
 
2.3%
B747
 
1.6%
G365
 
0.8%
C9
 
< 0.1%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
A
30106 
D
10710 
E
 
1628
B
 
1501
F
 
1299
Other values (3)
 
984

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46228
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A30106
65.1%
D10710
 
23.2%
E1628
 
3.5%
B1501
 
3.2%
F1299
 
2.8%
G571
 
1.2%
K267
 
0.6%
C146
 
0.3%

Length

2023-01-20T00:18:12.941622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:13.158306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a30106
65.1%
d10710
 
23.2%
e1628
 
3.5%
b1501
 
3.2%
f1299
 
2.8%
g571
 
1.2%
k267
 
0.6%
c146
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A30106
65.1%
D10710
 
23.2%
E1628
 
3.5%
B1501
 
3.2%
F1299
 
2.8%
G571
 
1.2%
K267
 
0.6%
C146
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter46228
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A30106
65.1%
D10710
 
23.2%
E1628
 
3.5%
B1501
 
3.2%
F1299
 
2.8%
G571
 
1.2%
K267
 
0.6%
C146
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin46228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A30106
65.1%
D10710
 
23.2%
E1628
 
3.5%
B1501
 
3.2%
F1299
 
2.8%
G571
 
1.2%
K267
 
0.6%
C146
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII46228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A30106
65.1%
D10710
 
23.2%
E1628
 
3.5%
B1501
 
3.2%
F1299
 
2.8%
G571
 
1.2%
K267
 
0.6%
C146
 
0.3%

booking_changes
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2643635892
Minimum0
Maximum21
Zeros37639
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:13.369624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7097132655
Coefficient of variation (CV)2.684610493
Kurtosis99.22564145
Mean0.2643635892
Median Absolute Deviation (MAD)0
Skewness6.575270874
Sum12221
Variance0.5036929192
MonotonicityNot monotonic
2023-01-20T00:18:13.503731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
037639
81.4%
16238
 
13.5%
21713
 
3.7%
3380
 
0.8%
4154
 
0.3%
534
 
0.1%
621
 
< 0.1%
718
 
< 0.1%
87
 
< 0.1%
144
 
< 0.1%
Other values (11)20
 
< 0.1%
ValueCountFrequency (%)
037639
81.4%
16238
 
13.5%
21713
 
3.7%
3380
 
0.8%
4154
 
0.3%
534
 
0.1%
621
 
< 0.1%
718
 
< 0.1%
87
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
161
 
< 0.1%
153
< 0.1%
144
< 0.1%
133
< 0.1%
121
 
< 0.1%
112
< 0.1%

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
No Deposit
46198 
Non Refund
 
24
Refundable
 
6

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters462280
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit46198
99.9%
Non Refund24
 
0.1%
Refundable6
 
< 0.1%

Length

2023-01-20T00:18:13.715506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:14.033612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no46198
50.0%
deposit46198
50.0%
non24
 
< 0.1%
refund24
 
< 0.1%
refundable6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o92420
20.0%
e46234
10.0%
N46222
10.0%
46222
10.0%
s46198
10.0%
i46198
10.0%
t46198
10.0%
p46198
10.0%
D46198
10.0%
n54
 
< 0.1%
Other values (7)138
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter323608
70.0%
Uppercase Letter92450
 
20.0%
Space Separator46222
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o92420
28.6%
e46234
14.3%
s46198
14.3%
i46198
14.3%
t46198
14.3%
p46198
14.3%
n54
 
< 0.1%
f30
 
< 0.1%
u30
 
< 0.1%
d30
 
< 0.1%
Other values (3)18
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N46222
50.0%
D46198
50.0%
R30
 
< 0.1%
Space Separator
ValueCountFrequency (%)
46222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin416058
90.0%
Common46222
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o92420
22.2%
e46234
11.1%
N46222
11.1%
s46198
11.1%
i46198
11.1%
t46198
11.1%
p46198
11.1%
D46198
11.1%
n54
 
< 0.1%
R30
 
< 0.1%
Other values (6)108
 
< 0.1%
Common
ValueCountFrequency (%)
46222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII462280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o92420
20.0%
e46234
10.0%
N46222
10.0%
46222
10.0%
s46198
10.0%
i46198
10.0%
t46198
10.0%
p46198
10.0%
D46198
10.0%
n54
 
< 0.1%
Other values (7)138
 
< 0.1%

agent
Real number (ℝ≥0)

MISSING

Distinct202
Distinct (%)0.5%
Missing5522
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean28.08151133
Minimum1
Maximum509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:14.216535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median9
Q314
95-th percentile152
Maximum509
Range508
Interquartile range (IQR)5

Descriptive statistics

Standard deviation56.32166282
Coefficient of variation (CV)2.005649275
Kurtosis21.39263731
Mean28.08151133
Median Absolute Deviation (MAD)2
Skewness4.227358035
Sum1143086
Variance3172.129703
MonotonicityNot monotonic
2023-01-20T00:18:14.408702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
918693
40.4%
73065
 
6.6%
142988
 
6.5%
11907
 
4.1%
61717
 
3.7%
281556
 
3.4%
8848
 
1.8%
3541
 
1.2%
37513
 
1.1%
83507
 
1.1%
Other values (192)8371
18.1%
(Missing)5522
 
11.9%
ValueCountFrequency (%)
11907
 
4.1%
233
 
0.1%
3541
 
1.2%
416
 
< 0.1%
61717
 
3.7%
73065
 
6.6%
8848
 
1.8%
918693
40.4%
10186
 
0.4%
11260
 
0.6%
ValueCountFrequency (%)
5098
< 0.1%
4957
< 0.1%
48410
< 0.1%
4801
 
< 0.1%
4761
 
< 0.1%
4758
< 0.1%
47417
< 0.1%
46712
< 0.1%
4641
 
< 0.1%
4612
 
< 0.1%

company
Real number (ℝ≥0)

MISSING

Distinct192
Distinct (%)6.6%
Missing43323
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean143.1958692
Minimum8
Maximum497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:14.615933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile40
Q140
median91
Q3219
95-th percentile408
Maximum497
Range489
Interquartile range (IQR)179

Descriptive statistics

Standard deviation119.9337592
Coefficient of variation (CV)0.8375504117
Kurtosis0.19955487
Mean143.1958692
Median Absolute Deviation (MAD)51
Skewness1.047480589
Sum415984
Variance14384.10659
MonotonicityNot monotonic
2023-01-20T00:18:14.867804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40847
 
1.8%
45222
 
0.5%
153167
 
0.4%
219132
 
0.3%
233103
 
0.2%
17499
 
0.2%
6792
 
0.2%
24259
 
0.1%
5152
 
0.1%
9144
 
0.1%
Other values (182)1088
 
2.4%
(Missing)43323
93.7%
ValueCountFrequency (%)
81
 
< 0.1%
914
 
< 0.1%
111
 
< 0.1%
146
 
< 0.1%
181
 
< 0.1%
351
 
< 0.1%
3837
 
0.1%
40847
1.8%
45222
 
0.5%
4625
 
0.1%
ValueCountFrequency (%)
4971
 
< 0.1%
4941
 
< 0.1%
4922
 
< 0.1%
4911
 
< 0.1%
4891
 
< 0.1%
4861
 
< 0.1%
48513
< 0.1%
4832
 
< 0.1%
4811
 
< 0.1%
4791
 
< 0.1%

days_in_waiting_list
Real number (ℝ≥0)

ZEROS

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.150082201
Minimum0
Maximum379
Zeros45127
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:15.070508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum379
Range379
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.57645979
Coefficient of variation (CV)8.174785028
Kurtosis148.3738321
Mean2.150082201
Median Absolute Deviation (MAD)0
Skewness11.1532541
Sum99394
Variance308.9319387
MonotonicityNot monotonic
2023-01-20T00:18:15.268166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045127
97.6%
58164
 
0.4%
8776
 
0.2%
6351
 
0.1%
3847
 
0.1%
17639
 
0.1%
7737
 
0.1%
22336
 
0.1%
4833
 
0.1%
9830
 
0.1%
Other values (64)588
 
1.3%
ValueCountFrequency (%)
045127
97.6%
15
 
< 0.1%
22
 
< 0.1%
414
 
< 0.1%
52
 
< 0.1%
612
 
< 0.1%
71
 
< 0.1%
92
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
3796
 
< 0.1%
33014
 
< 0.1%
25910
 
< 0.1%
23629
0.1%
2244
 
< 0.1%
22336
0.1%
2158
 
< 0.1%
20710
 
< 0.1%
18722
< 0.1%
17825
0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
Transient
32306 
Transient-Party
12463 
Contract
 
1195
Group
 
264

Length

Max length15
Median length9
Mean length10.56889764
Min length5

Characters and Unicode

Total characters488579
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient-Party
3rd rowTransient-Party
4th rowTransient-Party
5th rowTransient-Party

Common Values

ValueCountFrequency (%)
Transient32306
69.9%
Transient-Party12463
 
27.0%
Contract1195
 
2.6%
Group264
 
0.6%

Length

2023-01-20T00:18:15.440357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:15.748084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
transient32306
69.9%
transient-party12463
 
27.0%
contract1195
 
2.6%
group264
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n90733
18.6%
t59622
12.2%
r58691
12.0%
a58427
12.0%
T44769
9.2%
s44769
9.2%
i44769
9.2%
e44769
9.2%
y12463
 
2.6%
-12463
 
2.6%
Other values (7)17104
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter417425
85.4%
Uppercase Letter58691
 
12.0%
Dash Punctuation12463
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n90733
21.7%
t59622
14.3%
r58691
14.1%
a58427
14.0%
s44769
10.7%
i44769
10.7%
e44769
10.7%
y12463
 
3.0%
o1459
 
0.3%
c1195
 
0.3%
Other values (2)528
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T44769
76.3%
P12463
 
21.2%
C1195
 
2.0%
G264
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-12463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin476116
97.4%
Common12463
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n90733
19.1%
t59622
12.5%
r58691
12.3%
a58427
12.3%
T44769
9.4%
s44769
9.4%
i44769
9.4%
e44769
9.4%
y12463
 
2.6%
P12463
 
2.6%
Other values (6)4641
 
1.0%
Common
ValueCountFrequency (%)
-12463
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII488579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n90733
18.6%
t59622
12.2%
r58691
12.0%
a58427
12.0%
T44769
9.2%
s44769
9.2%
i44769
9.2%
e44769
9.2%
y12463
 
2.6%
-12463
 
2.6%
Other values (7)17104
 
3.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size361.3 KiB
0
44302 
1
 
1921
2
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46228
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
044302
95.8%
11921
 
4.2%
23
 
< 0.1%
32
 
< 0.1%

Length

2023-01-20T00:18:15.975203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-20T00:18:16.216251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
044302
95.8%
11921
 
4.2%
23
 
< 0.1%
32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
044302
95.8%
11921
 
4.2%
23
 
< 0.1%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46228
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
044302
95.8%
11921
 
4.2%
23
 
< 0.1%
32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common46228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
044302
95.8%
11921
 
4.2%
23
 
< 0.1%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII46228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
044302
95.8%
11921
 
4.2%
23
 
< 0.1%
32
 
< 0.1%

total_of_special_requests
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7410876525
Minimum0
Maximum5
Zeros21617
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:16.399159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8338522811
Coefficient of variation (CV)1.125173626
Kurtosis0.7991322246
Mean0.7410876525
Median Absolute Deviation (MAD)1
Skewness1.02196617
Sum34259
Variance0.6953096267
MonotonicityNot monotonic
2023-01-20T00:18:16.587583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
021617
46.8%
116699
36.1%
26403
 
13.9%
31307
 
2.8%
4177
 
0.4%
525
 
0.1%
ValueCountFrequency (%)
021617
46.8%
116699
36.1%
26403
 
13.9%
31307
 
2.8%
4177
 
0.4%
525
 
0.1%
ValueCountFrequency (%)
525
 
0.1%
4177
 
0.4%
31307
 
2.8%
26403
 
13.9%
116699
36.1%
021617
46.8%

stays_total
Real number (ℝ≥0)

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.923617721
Minimum0
Maximum57
Zeros308
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size361.3 KiB
2023-01-20T00:18:16.811072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum57
Range57
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.76218977
Coefficient of variation (CV)0.6027428818
Kurtosis55.05213447
Mean2.923617721
Median Absolute Deviation (MAD)1
Skewness3.406518854
Sum135153
Variance3.105312786
MonotonicityNot monotonic
2023-01-20T00:18:17.560954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
311895
25.7%
210992
23.8%
19169
19.8%
47704
16.7%
53221
 
7.0%
71251
 
2.7%
61116
 
2.4%
0308
 
0.7%
8209
 
0.5%
9120
 
0.3%
Other values (23)243
 
0.5%
ValueCountFrequency (%)
0308
 
0.7%
19169
19.8%
210992
23.8%
311895
25.7%
47704
16.7%
53221
 
7.0%
61116
 
2.4%
71251
 
2.7%
8209
 
0.5%
9120
 
0.3%
ValueCountFrequency (%)
571
< 0.1%
491
< 0.1%
481
< 0.1%
431
< 0.1%
341
< 0.1%
291
< 0.1%
281
< 0.1%
271
< 0.1%
241
< 0.1%
231
< 0.1%

Interactions

2023-01-20T00:17:51.715250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:06.118694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:08.902755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:12.264600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:15.532756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:18.332918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:21.177546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:24.353189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:29.611431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:32.260767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:38.195493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:45.601780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:48.423481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:51.945212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:06.305157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:09.076698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:12.432440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:15.798205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:18.490337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:21.342425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:24.594617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:29.806706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:32.609309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:38.581653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:45.773768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:48.589115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:52.170470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:06.483066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:09.238542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:12.585518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:16.165890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:18.829525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:21.504910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:24.914410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:29.978653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:32.956207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:39.020144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:45.938553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:48.741864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:52.458917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:06.636815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:09.404147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:12.899968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:16.377458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:18.977803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:21.657485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:25.168813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:30.143575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:33.312206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:39.370551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:46.098725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:48.930792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:52.738364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:06.820165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:09.747204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:13.056719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:16.551743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:19.128637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:21.805241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:25.511444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:30.288483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:33.749935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:39.891877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:46.311440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:49.125208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:53.026322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:06.986062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:10.068665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:13.220599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:16.699264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:19.280686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:21.951182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:25.829772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:30.453992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:34.158324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:40.421448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:46.477118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:49.301534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:53.261890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:07.158187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:10.292759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:13.378773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:16.852411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:19.434336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:22.098503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:26.150836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:30.616333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:34.968266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:40.964334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:46.634533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:49.473673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:53.520868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:07.319930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:10.512156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:13.551987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:17.015607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:19.598281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:22.263007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:26.380725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:30.788562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:35.380005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:42.105193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:46.845135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:49.655966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:53.871846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:07.509011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:10.726300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:13.718396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:17.175391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:19.757474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:22.458189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:26.565910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:30.954826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:35.810847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:42.629549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:47.065566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:49.828122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:54.619051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:08.025759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:11.188668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:14.203691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:17.537308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:20.168531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:22.966892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:27.597161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:31.345080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:36.517023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:43.407080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:47.523322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:50.332694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:56.179182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:08.444110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:11.683342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:14.697698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:17.881141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:20.648711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:23.743473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:28.571235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:31.758755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:37.123730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:44.149834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:47.895838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:50.802396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:56.485062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:08.600432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:11.905852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:15.023305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-20T00:17:23.963305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:29.044622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:31.942646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:37.513249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:44.612462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:48.061175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:51.154534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:56.734729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:08.752580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:12.111534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:15.248487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:18.183673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:20.986625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:24.175342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:29.361637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:32.109590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:37.852989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:45.223583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:48.219405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-20T00:17:51.487511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-01-20T00:17:58.697183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-20T00:18:02.855833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-20T00:18:04.974017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-20T00:18:05.462478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typerequired_car_parking_spacestotal_of_special_requestsstays_total
04006062015727110.00HBPRTOffline TA/TOTA/TO000AA0.0No Deposit6.0nan0Transient002
14006632015727210.00HBPRTGroupsTA/TO000AA1.0No Deposit1.0nan0Transient-Party003
240070432015727320.00HBPRTGroupsTA/TO000AA0.0No Deposit1.0nan0Transient-Party002
340071432015727320.00HBPRTGroupsTA/TO000AA1.0No Deposit1.0nan0Transient-Party002
440072432015727320.00HBPRTGroupsTA/TO000AA0.0No Deposit1.0nan0Transient-Party002
54007342015727310.00HBPRTGroupsTA/TO000AA0.0No Deposit1.0nan0Transient-Party002
640075432015727310.00HBPRTGroupsTA/TO000AA1.0No Deposit1.0nan0Transient-Party002
740077432015727320.00HBPRTGroupsTA/TO000AA0.0No Deposit1.0nan0Transient-Party002
840078432015727320.00HBPRTGroupsTA/TO000AA0.0No Deposit1.0nan0Transient-Party002
940082432015727320.00HBPRTGroupsTA/TO000AA0.0No Deposit1.0nan0Transient-Party002

Last rows

df_indexlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typerequired_car_parking_spacestotal_of_special_requestsstays_total
462181193804420178353120.00SCDEUOnline TATA/TO000AA0.0No Deposit9.0nan0Transient014
4621911938118820178353120.00BBDEUDirectDirect000AA0.0No Deposit14.0nan0Transient005
4622011938213520178353030.00BBJPNOnline TATA/TO000GG0.0No Deposit7.0nan0Transient006
4622111938316420178353120.00BBDEUOffline TA/TOTA/TO000AA0.0No Deposit42.0nan0Transient006
462221193842120178353020.00BBBELOffline TA/TOTA/TO000AA0.0No Deposit394.0nan0Transient027
462231193852320178353020.00BBBELOffline TA/TOTA/TO000AA0.0No Deposit394.0nan0Transient007
4622411938610220178353130.00BBFRAOnline TATA/TO000EE0.0No Deposit9.0nan0Transient027
462251193873420178353120.00BBDEUOnline TATA/TO000DD0.0No Deposit9.0nan0Transient047
4622611938810920178353120.00BBGBROnline TATA/TO000AA0.0No Deposit89.0nan0Transient007
4622711938920520178352920.00HBDEUOnline TATA/TO000AA0.0No Deposit9.0nan0Transient029